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Record W3206960943 · doi:10.1139/cjfas-2021-0153

Assessing wild genetic background and parental effects on size of growth hormone transgenic coho salmon

2021· article· en· W3206960943 on OpenAlexafffundvenue
Erin K. McClelland, Breanna M. Watson, L. Fredrik Sundström, Rosalind A. Leggatt, Dionne Sakhrani, Robert H. Devlin

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsVancouver Island UniversityFisheries and Oceans Canada
FundersFisheries and Oceans CanadaSimon Fraser University
KeywordsSireBiologyIntrogressionTransgeneOffspringTransgenesisOncorhynchusPopulationMatingZoologyGeneticsFish <Actinopterygii>Animal scienceGeneFisheryDemographyEmbryogenesis

Abstract

fetched live from OpenAlex

Experiments examining potential impacts of growth hormone (GH) transgenesis in fish typically use a single source strain and do not address potential differential impacts in strains of different genetic backgrounds. Here, we examine the effects of differing genetic backgrounds when reared in culture on the growth of transgenic and nontransgenic coho salmon (Oncorhynchus kisutch) produced by mating sires from different rivers with transgenic dams from a single origin. We found a significant difference in size between offspring of sires originating from various river systems in British Columbia. This difference was independent of differences between transgenotypes (i.e., transgenic vs. nontransgenic offspring). However, the effects of strain or sire were relatively small compared to the effects of the transgene, which were consistent regardless of sire origin. Thus, results derived from studies of GH transgenic fish from a single source population can provide useful information for assessments of GH transgenic salmon from other systems. This has important implications for examining potential risks from introgression of a transgene into different populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.244
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes3
Has abstractyes

Explore more

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